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Google_drive

by io.github.pipeworx-io

Deep Research

deep_research
Read-onlyIdempotent

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,743 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a hop field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).
questionYesThe research question, in natural language. Broad/multi-part is fine — decomposition is the point.

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent annotations, the description discloses account and paywall requirements, parallel facet routing across 5,743 tools, gaps[] behavior, contradictions[] for standard/thorough modes, citation_uri only when fetchable, semantic excerpting, and 15-90s latency. There is no contradiction with the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description front-loads the account requirement and the core definition before going into detail. It is long and somewhat repeats the depth-mode information already present in the schema, but the density is justified by the tool's complexity and the decision-relevant details it adds.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description fully explains what the agent will receive: a findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, hop, gaps[], and contradictions[]. It also covers latency, account requirements, and alternative routing, so an agent can invoke it confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers both parameters fully, but the description adds useful semantic context: the question should be broad/multi-part and over structured data, not a single lookup or news topic, and it gives concrete question examples. The depth parameter is connected to paid plans, gap recovery, and contradiction scans beyond the schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific operation: grounded multi-source research across Pipeworx's 1500 structured data sources in one call, with concrete examples. It also explicitly distinguishes itself from open-web search and from the sibling tool ask_pipeworx, so an agent can tell which tool to pick.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to use deep_research: best for broad/multi-part structured-data questions. It also gives clear when-not-to-use conditions: single lookups should use ask_pipeworx, breaking/current-news topics should prefer ask_pipeworx, and unauthenticated users should use ask_pipeworx. This is actionable and precise.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer queries, and the multiple polymarket_* tools scan for edges and arbitrage. The Drive tools are distinct, but the surrounding 31 unrelated tools create significant ambiguity about which tool to call for a given task.

Naming Consistency2/5

Tool names are all snake_case, but the pattern is inconsistent: some start with verbs (drive_create_file, ask_pipeworx, validate_claim), some are noun phrases (entity_profile, recent_changes, polymarket_edges), and some are bare verbs (remember, forget, recall). There is no uniform verb_noun convention, and suffixes like _beta and _grounded add further irregularity.

Tool Count1/5

36 tools is far too many for a server named Google_drive, especially since only 5 tools (drive_create_file, drive_get_content, drive_get_file, drive_list_files, drive_search) actually relate to Drive. The other 31 tools cover unrelated domains like Pipeworx data queries, Polymarket betting, and memory management, making the tool count wildly disproportionate to the server's apparent purpose.

Completeness2/5

For a Google Drive server, the tool set is incomplete: it covers create, get content, get metadata, list, and search, but lacks essential operations like updating, deleting, uploading, moving, copying files, creating folders, or managing permissions/sharing. These gaps would force agents to work around missing core Drive functionality.

Resources